✨ A plotter for reinforcement learning (RL)

📉 Visualize your Deep Learning training in static graphics

All about machine learning

Usages Examples for ML-Logger

Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

Implementation of Andrew Ng's machine learning exercises in Julia

An Implementation of Crawford's and Wright's Learning Curve Production Functions

Using Alexnet Architecture for Multiclass Fruit Classification

Nine diagnostic tools for detecting and understanding overfitting in scikit-learn models — polynomial overfitting, learning curves, validation curves, bias-variance decomposition, regularisation sweeps, data leakage detection, and more. Companion code for the ML Diagnostics Mastery series.

A new package that helps users compare and choose the right data analysis tool by providing structured, expert-level insights. Users input their specific data analysis needs, project requirements, or

Image processing with python and Machine learning. This is about analyzing Ayurveda plants and detection through uploading an image of plant leaves or flowers.

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Plotting cost curves of solar PV

Boston house prices prediction for machine learning nanodegree

The goal is to predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked, etc.

Image Analysis: Classification of hand poses in the American sign language

Interactive ML experimentation platform for understanding model behavior and statistical decision-making (A/B testing, bias-variance, learning curves).

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervise…